Understanding Causal Inference

A Guide to Causal Inference
Table of Contents
About-us
Preface
What is Causality?
Why bother with Causality?
Origin of Causality
Statistical Inference Vs Causal Inference
Decision-Making
Why we need Causality?
- Leaders in the Industry
Key Causal Terms and FAQ
Assumptions
- Causal Assumptions
Bias
- Selection Bias
- Correlation is not Causation
  Simpsons Paradox
Causal Graphs
Counterfactuals
- Potential Outcomes Framework
Modeling for Causal Inference
- Experimental Data
  Randomization
  Problems with Randomization
  A/B Testing
  Experiment
- Non-Experimental / Observational Data
  Instrumental Variables
  Weighting
  Inverse Propensity Weighting
  Propensity Score
  Sensitivity Analysis
  Regression Discontinuity
  Matching
  Stratification
  Methods
  Implications
Tools and Libraries
Limitations of Causal Inference
- Fundamental Problem of Causal Inference
Real-World Implementations
What's Next
References

Powered by GitBook

On this page

Was this helpful?

Mediators

PreviousConfounders NextBack Door Paths

Last updated 4 years ago

Mediators: These are the variables that **mediate the path between 2 variables(referred to treatment and outcome)

Mediators are an essential part of the causal Paths. Conditioning on mediators blocks the causal paths between the treatment and outcome.

Mediation analysis is the analysis of understanding the front door paths from treatment to the outcome.